The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

UniProt and Mass Spectrometry-Based Proteomics-A 2-Way Working Relationship

The human proteome comprises of all of the proteins produced by the sequences translated from the human genome with additional modifications in both sequence and function caused by nonsynonymous variants and posttranslational modifications including cleavage of the initial transcript into smaller peptides and polypeptides. The UniProtKB database (www.uniprot.org) is the world's leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information and presents a summary of experimentally verified, or computationally predicted, functional information added by our expert biocuration team for each protein in the proteome. Researchers in the field of mass spectrometry-based proteomics both consume and add to the body of data available in UniProtKB, and this review highlights the information we provide to this community and the knowledge we in turn obtain from groups via deposition of large-scale datasets in public domain databases.

Transfer learning for drug-target interaction prediction

MOTIVATION

Utilizing AI-driven approaches for drug-target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach.

RESULTS

Here, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets.

AVAILABILITY AND IMPLEMENTATION

The source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org.

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Searching and Navigating UniProt Databases

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. It provides 10 searchable datasets and four main tools. The key UniProt datasets are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), the UniProt Archive (UniParc), and protein sets for completely sequenced genomes (Proteomes). Other supporting datasets include information about proteins that is present in UniProtKB protein entries, such as literature citations, taxonomy, and subcellular locations, among others. This article focuses on how to use UniProt datasets. The first basic protocol describes navigation and searching mechanisms for the UniProt datasets, and two additional protocols build on the first protocol to describe advanced search and query building. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching UniProt datasets Basic Protocol 2: Advanced search and query building Basis Protocol 3: Adding parameters using advanced search.

RNY3 modulates cell proliferation and IL13 mRNA levels in a T lymphocyte model: a possible new epigenetic mechanism of IL-13 regulation

Allergic asthma is the most common type of asthma. It is characterized by T_H2 cell-driven inflammation in which interleukin-13 (IL-13) plays a pivotal role. Cytoplasmic RNAs (Y-RNAs), a variety of non-coding RNAs that are dysregulated in many cancer types, are also differentially expressed in patients with allergic asthma. Their function in the development of the disease is still unknown. We investigated the potential role of RNY3 RNA (hY3) in the T_H2 cell inflammatory response using the Jurkat cell line as a model. hY3 expression levels were modulated to mimic the upregulation effect in allergic disease. We evaluated the effect of hY3 over cell stimulation and the expression of the T_H2 cytokine IL13. Total RNA was isolated and retrotranscribed, and RNA levels were assessed by qPCR. In Jurkat cells, hY3 levels increased upon stimulation with phorbol 12-myristate 13-acetate (PMA) and ionomycin. When transfecting with high levels of hY3 mimic molecules, cell proliferation rate decreased while IL13 mRNA levels increased upon stimulation compared to stimulated control cells. Our results show the effect of increased hY3 levels on cell proliferation and the levels of IL13 mRNA in Jurkat cells. Also, we showed that hY3 could act over other cells via exosomes. This study opens up new ways to study the potential regulatory function of hY3 over IL-13 production and its implications for asthma development.

NOM fractionation by HPSEC-DAD-OCD for predicting trihalomethane disinfection by-product formation potential in full-scale drinking water treatment plants

Chlorination is a common method for water disinfection; however, it leads to the formation of disinfection by-products (DBPs), which are undesirable toxic pollutants. To prevent their formation, it is crucial to understand the reactivity of natural organic matter (NOM), which is considered a dominant precursor of DBPs. We propose a novel size exclusion chromatography (SEC) approach to evaluate NOM reactivity and the formation potential of total trihalomethanes-formation potentials (tTHMs-FP) and four regulated species (i.e. CHCl_3, CHBrCl_2, CHBr₂Cl, and CHBr₃). This method combines enhanced SEC separation with two analytical columns working in tandem and quantification of apparent molecular weight (AMW) NOM fractions using C content (organic carbon detector, OCD), 254-nm spectroscopic (diode-array detector, DAD) measurements, and spectral slopes at low (S_206-240) and high (S_350-380) wavelengths. Links between THMs-FP and NOM fractions from high performance size exclusion chromatography HPSEC-DAD-OCD were investigated using statistical modelling with multiple linear regressions for samples taken alongside conventional full-scale as well as full- and pilot-scale electrodialysis reversal and bench-scale ion exchange resins. The proposed models revealed promising correlations between the AMW NOM fractions and the THMs-FP. Methodological changes increased fractionated signal correlations relative to bulk regressions, especially in the proposed HPSEC-DAD-OCD method. Furthermore, spectroscopic models based on fractionated signals are presented, providing a promising approach to predict THMs-FP simultaneously considering the effect of the dominant THMs precursors, NOM and Br^-.

Hierarchical deep learning for predicting GO annotations by integrating protein knowledge

MOTIVATION

Experimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming and cannot cope with the exponential growth of data generated by high-throughput sequencing methods. Hence, researchers need reliable computational systems to help fill the gap with automatic function prediction. The results of the last Critical Assessment of Function Annotation challenge revealed that GO-terms prediction remains a very challenging task. Recent developments on deep learning are significantly breaking out the frontiers leading to new knowledge in protein research thanks to the integration of data from multiple sources. However, deep models hitherto developed for functional prediction are mainly focused on sequence data and have not achieved breakthrough performances yet.

RESULTS

We propose DeeProtGO, a novel deep-learning model for predicting GO annotations by integrating protein knowledge. DeeProtGO was trained for solving 18 different prediction problems, defined by the three GO sub-ontologies, the type of proteins, and the taxonomic kingdom. Our experiments reported higher prediction quality when more protein knowledge is integrated. We also benchmarked DeeProtGO against state-of-the-art methods on public datasets, and showed it can effectively improve the prediction of GO annotations.

AVAILABILITY AND IMPLEMENTATION

DeeProtGO and a case of use are available at https://github.com/gamerino/DeeProtGO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Quest for Orthologs orthology benchmark service in 2022

The Orthology Benchmark Service (https://orthology.benchmarkservice.org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium. It is an essential resource to compare existing and new methods of orthology inference (the bedrock for many comparative genomics and phylogenetic analysis) over a standard dataset and through common procedures. The Quest for Orthologs Consortium is dedicated to maintaining the resource up to date, through regular updates of the Reference Proteomes and increasingly accessible data through the OpenEBench platform. For this update, we have added a new benchmark based on curated orthology assertion from the Vertebrate Gene Nomenclature Committee, and provided an example meta-analysis of the public predictions present on the platform.

Profiling the Human Phosphoproteome to Estimate the True Extent of Protein Phosphorylation

Public phosphorylation databases such as PhosphoSitePlus (PSP) and PeptideAtlas (PA) compile results from published papers or openly available mass spectrometry (MS) data. However, there is no database-level control for false discovery of sites, likely leading to the overestimation of true phosphosites. By profiling the human phosphoproteome, we estimate the false discovery rate (FDR) of phosphosites and predict a more realistic count of true identifications. We rank sites into phosphorylation likelihood sets and analyze them in terms of conservation across 100 species, sequence properties, and functional annotations. We demonstrate significant differences between the sets and develop a method for independent phosphosite FDR estimation. Remarkably, we report estimated FDRs of 84, 98, and 82% within sets of phosphoserine (pSer), phosphothreonine (pThr), and phosphotyrosine (pTyr) sites, respectively, that are supported by only a single piece of identification evidence─the majority of sites in PSP. We estimate that around 62 000 Ser, 8000 Thr, and 12 000 Tyr phosphosites in the human proteome are likely to be true, which is lower than most published estimates. Furthermore, our analysis estimates that 86 000 Ser, 50 000 Thr, and 26 000 Tyr phosphosites are likely false-positive identifications, highlighting the significant potential of false-positive data to be present in phosphorylation databases.

Evaluation of Existing Methods for High-Order Epistasis Detection

Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions. Results show that in terms of detection power, the only methods that perform well across all experiments are the exhaustive methods, although their computational cost may be prohibitive in large-scale studies. Regarding non-exhaustive methods, not one could consistently find epistasis interactions when marginal effects are absent. If marginal effects are present, there are methods that perform well for high-order interactions, such as BADTrees, FDHE-IW, SingleMI or SNPHarvester. As for false-positive control, only SNPHarvester, FDHE-IW and DCHE show good results. The study concludes that there is no single epistasis detection method to recommend in all scenarios. Authors should prioritize exhaustive methods when sufficient computational resources are available considering the data set size, and resort to non-exhaustive methods when the analysis time is prohibitive.

A GO catalogue of human DNA-binding transcription factors

To control gene transcription, DNA-binding transcription factors recognise specific sequence motifs in gene regulatory regions. A complete and reliable GO annotation of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such information is demonstrated by the many lists of transcription factors that have been produced over the past decade. The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of over 1400 human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF. This catalogue has facilitated an improvement in the GO annotation of human DNA-binding transcription factors and led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

The Enzyme Portal: an integrative tool for enzyme information and analysis

Enzymes play essential roles in all life processes and are used extensively in the biomedical and biotechnological fields. However, enzyme-related information is spread across multiple resources making its retrieval time-consuming. In response to this challenge, the Enzyme Portal has been established to facilitate enzyme research, by providing a freely available hub where researchers can easily find and explore enzyme-related information. It integrates relevant enzyme data for a wide range of species from various resources such as UniProtKB, PDBe and ChEMBL. Here, we describe what type of enzyme-related data the Enzyme Portal provides, how the information is organized and, by show-casing two potential use cases, how to access and retrieve it.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Molecular Analysis of IL-5 Receptor Subunit Alpha as a Possible Pharmacogenetic Biomarker in Asthma

Background: Asthma is a heterogeneous syndrome with a broad clinical spectrum and high drug response variability. The inflammatory response in asthma involves multiple effector cells and mediator molecules. Based on asthma immunopathogenesis, precision medicine can be a promising strategy for identifying biomarkers. Biologic therapies acting on the IL-5/IL-5 receptor axis have been developed. IL-5 promotes proliferation, differentiation and activation of eosinophils by binding to the IL-5 receptor, located on the surface of eosinophils and basophils. This study aimed to investigate the expression of IL5RA in patients with several types of asthma and its expression after treatment with benralizumab, a biologic directed against IL-5 receptor subunit alpha.

Methods: Sixty peripheral blood samples, 30 from healthy controls and 30 from asthmatic patients, were selected for a transcriptomic RNAseq study. Differential expression analysis was performed by statistical assessment of fold changes and P-values. A validation study of IL5RA expression was developed using qPCR in 100 controls and 187 asthmatic patients. The effect of benralizumab on IL5RA expression was evaluated in five patients by comparing expression levels between pretreatment and after 3 months of treatment. The IL5RA mRNA levels were normalized to GAPDH and TBP expression values for each sample. Calculations were made by the comparative ΔΔCt method. All procedures followed the MIQE guidelines.

Results:IL5RA was one of the most differentially overexpressed coding transcripts in the peripheral blood of asthmatic patients (P = 8.63E-08 and fold change of 2.22). In the qPCR validation study, IL5RA expression levels were significantly higher in asthmatic patients than in controls (P < 0.001). Significant expression differences were present in different asthmatic types. In the biological drug study, patients treated with benralizumab showed a significant decrease in IL5RA expression and blood eosinophil counts. A notable improvement in ACT and lung function was also observed in these patients.

Conclusions: These results indicate that IL5RA is overexpressed in patients with different types of asthma. It could help identify which asthmatic patients will respond more efficiently to benralizumab, moving toward a more personalized asthma management. Although further studies are required, IL5RA could play a role as a biomarker and pharmacogenetic factor in asthma.

Genetics and Epigenetics of Nasal Polyposis: A Systematic Review

Chronic rhinosinusitis (CRS) is an inflammatory disease of the nose and paranasal sinuses that is often associated with nasal polyposis (CRSwNP) in the most severe cases. As in other complex diseases, genetic factors are thought to play an important role in the risk and development of the disease. Environment may also modulate the epigenetic signature in affected patients. In the present systematic review, we aimed to compile all published data on genetic and epigenetic variations in CRSwNP since 2000. We found 104 articles, 24 of which were related to epigenetic studies. We identified more than 150 genetic variants in 99 genes involved in the pathogenesis of nasal polyposis. These were clustered into 8 main networks, linking genes involved in inflammation and immune response (eg, MHC), cytokine genes (eg, TNF), leukotriene metabolism, and the extracellular matrix. A total of 89 miRNAs were also identified; these are associated mainly with biological functions such as the cell cycle, inflammation, and the immune response. We propose a potential relationship between genes and the miRNAs identified that may open new lines of investigation. An in-depth knowledge of gene variants and epigenetic traits could help us to design more tailored treatment for patients with CRSwNP.

The Quest for Orthologs benchmark service and consensus calls in 2020

The identification of orthologs—genes in different species which descended from the same gene in their last common ancestor—is a prerequisite for many analyses in comparative genomics and molecular evolution. Numerous algorithms and resources have been conceived to address this problem, but benchmarking and interpreting them is fraught with difficulties (need to compare them on a common input dataset, absence of ground truth, computational cost of calling orthologs). To address this, the Quest for Orthologs consortium maintains a reference set of proteomes and provides a web server for continuous orthology benchmarking (http://orthology.benchmarkservice.org). Furthermore, consensus ortholog calls derived from public benchmark submissions are provided on the Alliance of Genome Resources website, the joint portal of NIH-funded model organism databases.

Genetics and Epigenetics of Atopic Dermatitis: An Updated Systematic Review

BACKGROUND

Atopic dermatitis is a common inflammatory skin disorder that affects up to 15-20% of the population and is characterized by recurrent eczematous lesions with intense itching. As a heterogeneous disease, multiple factors have been suggested to explain the nature of atopic dermatitis (AD), and its high prevalence makes it necessary to periodically compile and update the new information available. In this systematic review, the focus is set at the genetic and epigenetic studies carried out in the last years.

METHODS

A systematic literature review was conducted in three scientific publication databases (PubMed, Cochrane Library, and Scopus). The search was restricted to publications indexed from July 2016 to December 2019, and keywords related to atopic dermatitis genetics and epigenetics were used.

RESULTS

A total of 73 original papers met the inclusion criteria established, including 9 epigenetic studies. A total of 62 genes and 5 intergenic regions were described as associated with AD.

CONCLUSION

Filaggrin (FLG) polymorphisms are confirmed as key genetic determinants for AD development, but also epigenetic regulation and other genes with functions mainly related to the immune system and extracellular matrix, reinforcing the notion of skin homeostasis breakage in AD.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BACKGROUND

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.

RESULTS

Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.

CONCLUSION

We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

UniProt genomic mapping for deciphering functional effects of missense variants

Understanding the association of genetic variation with its functional consequences in proteins is essential for the interpretation of genomic data and identifying causal variants in diseases. Integration of protein function knowledge with genome annotation can assist in rapidly comprehending genetic variation within complex biological processes. Here, we describe mapping UniProtKB human sequences and positional annotations, such as active sites, binding sites, and variants to the human genome (GRCh38) and the release of a public genome track hub for genome browsers. To demonstrate the power of combining protein annotations with genome annotations for functional interpretation of variants, we present specific biological examples in disease-related genes and proteins. Computational comparisons of UniProtKB annotations and protein variants with ClinVar clinically annotated single nucleotide polymorphism (SNP) data show that 32% of UniProtKB variants colocate with 8% of ClinVar SNPs. The majority of colocated UniProtKB disease-associated variants (86%) map to 'pathogenic' ClinVar SNPs. UniProt and ClinVar are collaborating to provide a unified clinical variant annotation for genomic, protein, and clinical researchers. The genome track hubs, and related UniProtKB files, are downloadable from the UniProt FTP site and discoverable as public track hubs at the UCSC and Ensembl genome browsers.

Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer's Disease Data

The analysis and interpretation of high-throughput datasets relies on access to high-quality bioinformatics resources, as well as processing pipelines and analysis tools. Gene Ontology (GO, geneontology.org) is a major resource for gene enrichment analysis. The aim of this project, funded by the Alzheimer's Research United Kingdom (ARUK) foundation and led by the University College London (UCL) biocuration team, was to enhance the GO resource by developing new neurological GO terms, and use GO terms to annotate gene products associated with dementia. Specifically, proteins and protein complexes relevant to processes involving amyloid-beta and tau have been annotated and the resulting annotations are denoted in GO databases as 'ARUK-UCL'. Biological knowledge presented in the scientific literature was captured through the association of GO terms with dementia-relevant protein records; GO itself was revised, and new GO terms were added. This literature biocuration increased the number of Alzheimer's-relevant gene products that were being associated with neurological GO terms, such as 'amyloid-beta clearance' or 'learning or memory', as well as neuronal structures and their compartments. Of the total 2055 annotations that we contributed for the prioritised gene products, 526 have associated proteins and complexes with neurological GO terms. To ensure that these descriptive annotations could be provided for Alzheimer's-relevant gene products, over 70 new GO terms were created. Here, we describe how the improvements in ontology development and biocuration resulting from this initiative can benefit the scientific community and enhance the interpretation of dementia data.

MPIGeneNet: Parallel Calculation of Gene Co-Expression Networks on Multicore Clusters

In this work, we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on two different systems using three typical input datasets shows that MPIGeneNet is significantly faster than RMTGeneNet. As an example, our tool is up to 175.41 times faster on a cluster with eight nodes, each one containing two 12-core Intel Haswell processors. The source code of MPIGeneNet, as well as a reference manual, are available at https://sourceforge.net/projects/mpigenenet/.

Expanding the horizons of microRNA bioinformatics

MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult for researchers to find and analyze microRNA-related experimental data and define further research projects. We are addressing this problem by providing two new bioinformatics data sets that contain experimentally verified functional information for mammalian microRNAs involved in cardiovascular-relevant, and other, processes. To date, our resource provides over 4400 Gene Ontology annotations associated with over 500 microRNAs from human, mouse, and rat and over 2400 experimentally validated microRNA:target interactions. We illustrate how this resource can be used to create microRNA-focused interaction networks with a biological context using the known biological role of microRNAs and the mRNAs they regulate, enabling discovery of associations between gene products, biological pathways and, ultimately, diseases. This data will be crucial in advancing the field of microRNA bioinformatics and will establish consistent data sets for reproducible functional analysis of microRNAs across all biological research areas.

ProtVista: visualization of protein sequence annotations

Summary: ProtVista is a comprehensive visualization tool for the graphical representation of protein sequence features in the UniProt Knowledgebase, experimental proteomics and variation public datasets. The complexity and relationships in this wealth of data pose a challenge in interpretation. Integrative visualization approaches such as provided by ProtVista are thus essential for researchers to understand the data and, for instance, discover patterns affecting function and disease associations.

Availability and Implementation: ProtVista is a JavaScript component released as an open source project under the Apache 2 License. Documentation and source code are available at http://ebi-uniprot.github.io/ProtVista/.

Contact:martin@ebi.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

ProForma: A Standard Proteoform Notation

The Consortium for Top-Down Proteomics (CTDP) proposes a standardized notation, ProForma, for writing the sequence of fully characterized proteoforms. ProForma provides a means to communicate any proteoform by writing the amino acid sequence using standard one-letter notation and specifying modifications or unidentified mass shifts within brackets following certain amino acids. The notation is unambiguous, human-readable, and can easily be parsed and written by bioinformatic tools. This system uses seven rules and supports a wide range of possible use cases, ensuring compatibility and reproducibility of proteoform annotations. Standardizing proteoform sequences will simplify storage, comparison, and reanalysis of proteomic studies, and the Consortium welcomes input and contributions from the research community on the continued design and maintenance of this standard.

Using the Gene Ontology to Annotate Key Players in Parkinson's Disease

The Gene Ontology (GO) is widely recognised as the gold standard bioinformatics resource for summarizing functional knowledge of gene products in a consistent and computable, information-rich language. GO describes cellular and organismal processes across all species, yet until now there has been a considerable gene annotation deficit within the neurological and immunological domains, both of which are relevant to Parkinson’s disease. Here we introduce the Parkinson’s disease GO Annotation Project, funded by Parkinson’s UK and supported by the GO Consortium, which is addressing this deficit by providing GO annotation to Parkinson’s-relevant human gene products, principally through expert literature curation. We discuss the steps taken to prioritise proteins, publications and cellular processes for annotation, examples of how GO annotations capture Parkinson’s-relevant information, and the advantages that a topic-focused annotation approach offers to users. Building on the existing GO resource, this project collates a vast amount of Parkinson’s-relevant literature into a set of high-quality annotations to be utilized by the research community.

Experimental Observation of Convective Cell Formation due to a Fast Wave Antenna in the Large Plasma Device

An experiment in a linear device, the Large Plasma Device, is used to study sheaths caused by an actively powered radio frequency (rf) antenna. The rf antenna used in the experiment consists of a single current strap recessed inside a copper box enclosure without a Faraday screen. A large increase in the plasma potential was observed along magnetic field lines that connect to the antenna limiter. The electric field from the spatial variation of the rectified plasma potential generated E[over →]×B[over →]_{0} flows, often referred to as convective cells. The presence of the flows generated by these potentials is confirmed by Mach probes. The observed convective cell flows are seen to cause the plasma in front of the antenna to flow away and cause a density modification near the antenna edge. These can cause hot spots and damage to the antenna and can result in a decrease in the ion cyclotron range of frequencies antenna coupling.

An update on the Enzyme Portal: an integrative approach for exploring enzyme knowledge

Enzymes are a key part of life processes and are increasingly important for various areas of research such as medicine, biotechnology, bioprocessing and drug research. The goal of the Enzyme Portal is to provide an interface to all European Bioinformatics Institute (EMBL-EBI) data about enzymes (de Matos, P., et al., (2013), BMC Bioinformatics, 14 (1), 103). These data include enzyme function, sequence features and family classification, protein structure, reactions, pathways, small molecules, diseases and the associated literature. The sources of enzyme data are: the UniProt Knowledgebase (UniProtKB) (UniProt Consortium, 2015), the Protein Data Bank in Europe (PDBe), (Valenkar, S., et al., Nucleic Acids Res.2016; 44, D385–D395) Rhea—a database of enzyme-catalysed reactions (Morgat, A., et al., Nucleic Acids Res. 2015; 43, D459-D464), Reactome—a database of biochemical pathways (Fabregat, A., et al., Nucleic Acids Res. 2016; 44, D481–D487), IntEnz—a resource with enzyme nomenclature information (Fleischmann, A., et al., Nucleic Acids Res. 2004 32, D434–D437) and ChEBI (Hastings, J., et al., Nucleic Acids Res. 2013) and ChEMBL (Bento, A. P., et al., Nucleic Acids Res. 201442, 1083–1090)—resources which contain information about small-molecule chemistry and bioactivity. This article describes the redesign of Enzyme Portal and the increased functionality added to maximise integration and interpretation of these data. Use case examples of the Enzyme Portal and the versatile workflows its supports are illustrated. We welcome the suggestion of new resources for integration.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

Uniclust databases of clustered and deeply annotated protein sequences and alignments

We present three clustered protein sequence databases, Uniclust90, Uniclust50, Uniclust30 and three databases of multiple sequence alignments (MSAs), Uniboost10, Uniboost20 and Uniboost30, as a resource for protein sequence analysis, function prediction and sequence searches. The Uniclust databases cluster UniProtKB sequences at the level of 90%, 50% and 30% pairwise sequence identity. Uniclust90 and Uniclust50 clusters showed better consistency of functional annotation than those of UniRef90 and UniRef50, owing to an optimised clustering pipeline that runs with our MMseqs2 software for fast and sensitive protein sequence searching and clustering. Uniclust sequences are annotated with matches to Pfam, SCOP domains, and proteins in the PDB, using our HHblits homology detection tool. Due to its high sensitivity, Uniclust contains 17% more Pfam domain annotations than UniProt. Uniboost MSAs of three diversities are built by enriching the Uniclust30 MSAs with local sequence matches from MMseqs2 profile searches through Uniclust30. All databases can be downloaded from the Uniclust server at uniclust.mmseqs.com. Users can search clusters by keywords and explore their MSAs, taxonomic representation, and annotations. Uniclust is updated every two months with the new UniProt release.

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

BACKGROUND

A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.

RESULTS

We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.

CONCLUSIONS

The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

Assessment of a rapid liquid-based cytology method for measuring sputum cell counts

Differential sputum cell counting is not widely available despite proven clinical utility in the management of asthma. We compared eosinophil counts obtained using liquid-based cytology (LBC), a routine histopathological processing method, and the current standard method. Eosinophil counts obtained using LBC were a strong predictor of sputum eosinophilia (≥3%) determined by the standard method suggesting LBC could be used in the management of asthma.

UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB

MOTIVATION

Similarity-based methods have been widely used in order to infer the properties of genes and gene products containing little or no experimental annotation. New approaches that overcome the limitations of methods that rely solely upon sequence similarity are attracting increased attention. One of these novel approaches is to use the organization of the structural domains in proteins.

RESULTS

We propose a method for the automatic annotation of protein sequences in the UniProt Knowledgebase (UniProtKB) by comparing their domain architectures, classifying proteins based on the similarities and propagating functional annotation. The performance of this method was measured through a cross-validation analysis using the Gene Ontology (GO) annotation of a sub-set of UniProtKB/Swiss-Prot. The results demonstrate the effectiveness of this approach in detecting functional similarity with an average F-score: 0.85. We applied the method on nearly 55.3 million uncharacterized proteins in UniProtKB/TrEMBL resulted in 44 818 178 GO term predictions for 12 172 114 proteins. 22% of these predictions were for 2 812 016 previously non-annotated protein entries indicating the significance of the value added by this approach.

AVAILABILITY AND IMPLEMENTATION

The results of the method are available at: ftp://ftp.ebi.ac.uk/pub/contrib/martin/DAAC/ CONTACT: tdogan@ebi.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.

From data repositories to submission portals: rethinking the role of domain-specific databases in CollecTF

Domain-specific databases are essential resources for the biomedical community, leveraging expert knowledge to curate published literature and provide access to referenced data and knowledge. The limited scope of these databases, however, poses important challenges on their infrastructure, visibility, funding and usefulness to the broader scientific community. CollecTF is a community-oriented database documenting experimentally validated transcription factor (TF)-binding sites in the Bacteria domain. In its quest to become a community resource for the annotation of transcriptional regulatory elements in bacterial genomes, CollecTF aims to move away from the conventional data-repository paradigm of domain-specific databases. Through the adoption of well-established ontologies, identifiers and collaborations, CollecTF has progressively become also a portal for the annotation and submission of information on transcriptional regulatory elements to major biological sequence resources (RefSeq, UniProtKB and the Gene Ontology Consortium). This fundamental change in database conception capitalizes on the domain-specific knowledge of contributing communities to provide high-quality annotations, while leveraging the availability of stable information hubs to promote long-term access and provide high-visibility to the data. As a submission portal, CollecTF generates TF-binding site information through direct annotation of RefSeq genome records, definition of TF-based regulatory networks in UniProtKB entries and submission of functional annotations to the Gene Ontology. As a database, CollecTF provides enhanced search and browsing, targeted data exports, binding motif analysis tools and integration with motif discovery and search platforms. This innovative approach will allow CollecTF to focus its limited resources on the generation of high-quality information and the provision of specialized access to the data.Database URL: http://www.collectf.org/.

Thermodynamic temperature assignment to the point of inflection of the melting curve of high-temperature fixed points

The thermodynamic temperature of the point of inflection of the melting transition of Re-C, Pt-C and Co-C eutectics has been determined to be 2747.84 ± 0.35 K, 2011.43 ± 0.18 K and 1597.39 ± 0.13 K, respectively, and the thermodynamic temperature of the freezing transition of Cu has been determined to be 1357.80 ± 0.08 K, where the ± symbol represents 95% coverage. These results are the best consensus estimates obtained from measurements made using various spectroradiometric primary thermometry techniques by nine different national metrology institutes. The good agreement between the institutes suggests that spectroradiometric thermometry techniques are sufficiently mature (at least in those institutes) to allow the direct realization of thermodynamic temperature above 1234 K (rather than the use of a temperature scale) and that metal-carbon eutectics can be used as high-temperature fixed points for thermodynamic temperature dissemination. The results directly support the developing mise en pratique for the definition of the kelvin to include direct measurement of thermodynamic temperature.

Dissemination of thermodynamic temperature above the freezing point of silver

The mise-en-pratique for the definition of the kelvin at high temperatures will formally allow dissemination of thermodynamic temperature either directly or mediated through high-temperature fixed points (HTFPs). In this paper, these two distinct dissemination methods are evaluated, namely source-based and detector-based. This was achieved by performing two distinct dissemination trials: one based on HTFPs, the other based on absolutely calibrated radiation thermometers or filter radiometers. These trials involved six national metrology institutes in Europe in the frame of the European Metrology Research Programme joint project 'Implementing the new kelvin' (InK). The results have shown that both dissemination routes are possible, with similar standard uncertainties of 1-2 K, over the range 1273-2773 K, showing that, depending on the facilities available in the laboratory, it will soon be possible to disseminate thermodynamic temperatures above 1273 K to users by either of the two methods with uncertainties comparable to the current temperature scale.

UniProt Tools

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data (UniProt Consortium, 2015). The UniProt Web site receives ∼400,000 unique visitors per month and is the primary means to access UniProt. Along with various datasets that you can search, UniProt provides three main tools. These are the 'BLAST' tool for sequence similarity searching, the 'Align' tool for multiple sequence alignment, and the 'Retrieve/ID Mapping' tool for using a list of identifiers to retrieve UniProtKB proteins and to convert database identifiers from UniProt to external databases or vice versa. This unit provides three basic protocols, three alternate protocols, and two support protocols for using UniProt tools.

Evaluation of a fibrate, specific stimulant of PPARα, as a therapeutic alternative to the treatment of clinical ovine pregnancy toxaemia

Ovine pregnancy toxaemia is a metabolic disorder affecting sheep in their last 6 weeks of pregnancy as a result of their inability to maintain adequate energy homoeostasis. Different alternative treatments are available with variable results. The aim of this research was to evaluate a peroxisome proliferator-activated receptor alpha (PPARα) stimulant as an alternative to treat clinical pregnancy toxaemia. Thirty-three adult sheep, with known gestation date and carrying a single foetus, were fasted from day 130 of gestation until animals showed clinical disease. From that moment onwards, sheep were treated during 6 days with three different therapeutic alternatives: 10 mg/kg of 2-methyl-2-phenoxy-propionic acid; 10 mg/kg of 2-methyl-2-phenoxy-propionic acid + 100 mL of propylene glycol oral; or 100 mL of propylene glycol oral. Glycaemia and serum β-hydroxybutyrate (BHOB) were determined daily. Liver biopsies were taken at day 130 of gestation, at the beginning and end of treatments and at 5 days postpartum, evaluating the extent and degree of the steatosis lesion. Even though in sheep treated with 2-methyl-2-phenoxy-propionic acid, serum concentrations of glucose and BHOB recovered more slowly, we conclude that 2-methyl-2-phenoxy-propionic acid alone or combined with propylene glycol can be used as an alternative to effectively treat fatty liver, and therefore pregnancy toxaemia.

Building a Beneficial Microbiome from Birth

The microbiota has recently been recognized as a driver of health that affects the immune, nervous, and metabolic systems. This influence is partially exerted through the metabolites produced, which may be relevant for optimal infant development and health. The gut microbiota begins developing early in life, and this initial colonization is remarkably important because it may influence long-term microbiota composition and activity. Considering that the microbiome may play a key role in health and disease, maintaining a protective microbiota could be critical in preventing dysbiosis-related diseases such as allergies, autoimmunity disorders, and metabolic syndrome. Breast milk and milk glycans in particular are thought to play a major role in shaping the early-life microbiota and promoting its development, thus affecting health. This review describes some of the effects the microbiota has on the host and discusses the role microbial metabolites play in shaping newborn health and development. We describe the gut microbiota structure and function during early life and the factors that determine its composition and hypothesize about the effects of human milk oligosaccharides and other prebiotic fibers on the neonatal microbiota.

The utility of exhaled nitric oxide in patients with suspected asthma

The value of FENO measurements in patients with symptoms suggestive of asthma is unclear. We performed an observational study to assess the ability of FENO to diagnose asthma and to predict response to inhaled corticosteroids (ICS). Our findings suggest FENO is not useful for asthma diagnosis but is accurate at predicting ICS response.

Host-virus interactions in hepatitis B and hepatitis C infection

Hepatitis B virus (HBV) and hepatitis C virus (HCV) are among the most endemic pathogens worldwide, with more than 500 million people globally currently infected with these viruses. These pathogens can cause acute and chronic hepatitis that progress to liver cirrhosis or hepatocellular carcinoma. Both viruses utilize multifaceted strategies to evade the host surveillance system and fall below the immunological radar. HBV has developed specific strategies to evade recognition by the innate immune system and is acknowledged to be a stealth virus. However, extensive research has revealed that HBV is recognized by dendritic cells (DCs) and natural killer (NK) cells. Indoleamine-2, 3-dioxygenase is an enforcer of sequential immune reactions in acute hepatitis B, and this molecule has been shown to be induced by the interaction of HBV-infected hepatocytes, DCs, and NK cells. The interleukin-28B genotype has been reported to influence HCV eradication either therapeutically or spontaneously, but the biological function of its gene product, a type-III interferon (IFN-λ3), remains to be elucidated. Human BDCA3(+)DCs have also been shown to be a potent producer of IFN-λ3 in HCV infection, suggesting the possibility that BDCA3(+)DCs could play a key role in developing therapeutic HCV vaccine. Here we review the current state of research on immune responses against HBV and HCV infection, with a specific focus on innate immunity. A comprehensive study based on clinical samples is urgently needed to improve our understanding of the immune mechanisms associated with viral control and thus to develop novel immune modulatory therapies to cure chronic HBV and HCV infection.

Maternal Diabetes and Cognitive Performance in the Offspring: A Systematic Review and Meta-Analysis

Objective

Diabetes during gestation is one of the most common pregnancy complications associated with adverse health effects for the mother and the child. Maternal diabetes has been proposed to negatively affect the cognitive abilities of the child, but experimental research assessing its impact is conflicting. The main aim of our study was to compare the cognitive function in children of diabetic and healthy pregnant women.

Methods

A systematic review and meta-analysis was conducted through a literature search using different electronic databases from the index date to January 31, 2015. We included studies that assessed the cognitive abilities in children (up to 14 years) of diabetic and non-diabetic mothers using standardized and validated neuropsychological tests.

Results

Of 7,698 references reviewed, 12 studies involving 6,140 infants met our inclusion criteria and contributed to meta-analysis. A random effect model was used to compute the standardized mean differences and 95% confidence interval (CI) were calculated. Infants (1–2 years) of diabetic mothers had significantly lower scores of mental and psychomotor development compared to control infants. The effect size for mental development was -0.41 (95% CI -0.59, -0.24; p<0.0001) and for psychomotor development was -0.31 (95% CI -0.55, -0.07; p = 0.0125) with non-significant heterogeneity. Diabetes during pregnancy could be associated with decreased intelligence quotient scores in school-age children, although studies showed significant heterogeneity.

Conclusion

The association between maternal diabetes and deleterious effects on mental/psychomotor development and overall intellectual function in the offspring must be taken with caution. Results are based on observational cohorts and a direct causal influence of intrauterine hyperglycemia remains uncertain. Therefore, more trials that include larger populations are warranted to elucidate whether gestational diabetes mellitus (GDM) has a negative impact on offspring central nervous system (CNS).

Searching and Navigating UniProt Databases

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt Web site receives ∼400,000 unique visitors per month and is the primary means to access UniProt. It provides ten searchable datasets and three main tools. The key UniProt datasets are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), the UniProt Archive (UniParc), and protein sets for completely sequenced genomes (Proteomes). Other supporting datasets include information about proteins that is present in UniProtKB protein entries such as literature citations, taxonomy, and subcellular locations, among others. This paper focuses on how to use UniProt datasets. The basic protocol describes navigation and searching mechanisms for the UniProt datasets, while two alternative protocols build on the basic protocol to describe advanced search and query building.

A resistively heated CeB6 emissive probe

The plasma potential, V(p), is a key quantity in experimental plasma physics. Its spatial gradients directly yield the electrostatic field present. Emissive probes operating under space-charge limited emission conditions float close to V(p) even under time-varying conditions. Throughout their long history in plasma physics, they have mostly been constructed with resistively heated tungsten wire filaments. In high density plasmas (>10(12) cm(-3)), hexaboride emitters are required because tungsten filaments cannot be heated to sufficient emission without component failure. A resistively heated emissive probe with a cerium hexaboride, CeB6, emitter has been developed to work in plasma densities up to 10(13) cm(-3). To show functionality, three spatial profiles of V(p) are compared using the emissive probe, a cold floating probe, and a swept probe inside a plasma containing regions with and without current. The swept probe and emissive probe agree well across the profile while the floating cold probe fails in the current carrying region.

Optimizing chemical conditioning for odour removal of undigested sewage sludge in drying processes

Emission of odours during the thermal drying in sludge handling processes is one of the main sources of odour problems in wastewater treatment plants. The objective of this work was to assess the use of the response surface methodology as a technique to optimize the chemical conditioning process of undigested sewage sludges, in order to improve the dewaterability, and to reduce the odour emissions during the thermal drying of the sludge. Synergistic effects between inorganic conditioners (iron chloride and calcium oxide) were observed in terms of sulphur emissions and odour reduction. The developed quadratic models indicated that optimizing the conditioners dosage is possible to increase a 70% the dewaterability, reducing a 50% and 54% the emission of odour and volatile sulphur compounds respectively. The optimization of the conditioning process was validated experimentally.

Analysis of the tryptic search space in UniProt databases

In this article, we provide a comprehensive study of the content of the Universal Protein Resource (UniProt) protein data sets for human and mouse. The tryptic search spaces of the UniProtKB (UniProt knowledgebase) complete proteome sets were compared with other data sets from UniProtKB and with the corresponding International Protein Index, reference sequence, Ensembl, and UniRef100 (where UniRef is UniProt reference clusters) organism-specific data sets. All protein forms annotated in UniProtKB (both the canonical sequences and isoforms) were evaluated in this study. In addition, natural and disease-associated amino acid variants annotated in UniProtKB were included in the evaluation. The peptide unicity was also evaluated for each data set. Furthermore, the peptide information in the UniProtKB data sets was also compared against the available peptide-level identifications in the main MS-based proteomics repositories. Identifying the peptides observed in these repositories is an important resource of information for protein databases as they provide supporting evidence for the existence of otherwise predicted proteins. Likewise, the repositories could use the information available in UniProtKB to direct reprocessing efforts on specific sets of peptides/proteins of interest. In summary, we provide comprehensive information about the different organism-specific sequence data sets available from UniProt, together with the pros and cons for each, in terms of search space for MS-based bottom-up proteomics workflows. The aim of the analysis is to provide a clear view of the tryptic search space of UniProt and other protein databases to enable scientists to select those most appropriate for their purposes.

The GOA database: gene Ontology annotation updates for 2015

The Gene Ontology Annotation (GOA) resource (http://www.ebi.ac.uk/GOA) provides evidence-based Gene Ontology (GO) annotations to proteins in the UniProt Knowledgebase (UniProtKB). Manual annotations provided by UniProt curators are supplemented by manual and automatic annotations from model organism databases and specialist annotation groups. GOA currently supplies 368 million GO annotations to almost 54 million proteins in more than 480 000 taxonomic groups. The resource now provides annotations to five times the number of proteins it did 4 years ago. As a member of the GO Consortium, we adhere to the most up-to-date Consortium-agreed annotation guidelines via the use of quality control checks that ensures that the GOA resource supplies high-quality functional information to proteins from a wide range of species. Annotations from GOA are freely available and are accessible through a powerful web browser as well as a variety of annotation file formats.